Gamification has been widely adopted to motivate consumers and enhance their experiences with products, services and brands. However, its effectiveness remains inconsistent: when applied superficially, it may be dismissed as a gimmick; and when misused, it may manipulate consumers and lead to negative outcomes. This study therefore develops a comprehensive understanding of how gamification works in marketing, which forms are most effective and what ethical dilemmas it entails.
This study systematically reviews 172 empirical studies across 149 papers, constituting the corpus of gamification marketing as of the end of 2024. The review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the TCCM (Theory–Context–Characteristics–Methodology) framework.
The findings indicate that gamification has been studied across seven distinct marketing contexts. It can improve key marketing performance dimensions (e.g. purchase intentions and loyalty), although its effectiveness varies by form. These effects typically operate through mediators (e.g. perceived value) and are shaped by moderators (e.g. prior experience). Based on these findings, 14 future research directions are proposed, spanning theoretical development, thematic exploration and methodological innovation.
Practitioners should tailor gamification designs to specific marketing objectives and contexts rather than relying on one-size-fits-all approaches. They should also implement ethical, consumer-centered gamification strategies that safeguard autonomy and privacy while avoiding unintended manipulation.
By synthesizing 15 years of research on gamification in marketing, this study offers an overarching view of how gamification influences consumer psychology and behavior. It consolidates existing knowledge, identifies critical gaps and outlines a strategic agenda for advancing gamified marketing research.
1. Introduction
In today's digitally driven landscape, businesses face unprecedented challenges in capturing consumer attention, driving purchase intentions and fostering consumer loyalty. Gamification is a transformative and engaging motivational information system through which everyday activities, services and systems are transformed into more gameful and engaging experiences (Hamari, 2019; Koivisto and Hamari, 2019; Xi and Hamari, 2020) and has emerged as a widely adopted tool to address these challenges. By tapping into human needs such as achievement, social interaction and competence, gamification turns marketing into active, interactive and rewarding experiences (Eppmann et al., 2018). The global gamification market reflects this momentum, with projections indicating growth from USD 29.11 billion in 2025 to USD 92.51 billion by 2030 (Mordor Intelligence, 2025).
Academic research provides encouraging evidence that gamification can improve key marketing performance dimensions such as brand attitude (Zhu et al., 2021) and purchase intentions (Yu and Huang, 2022). However, despite its promise, critical perspectives caution against unreflective adoption. Poorly designed implementations can cause user fatigue, disengagement or even negative brand perceptions (Yang et al., 2024). Moreover, gamification may blur the line between motivation and manipulation by exploiting psychological triggers such as excitement, enjoyment and flow (Koivisto and Hamari, 2019). When consumers perceive gamification as superficial or overly persuasive, they may dismiss it as deceptive, ultimately undermining its effectiveness. These dual possibilities underscore the need for a synthesized overview of gamification in marketing.
Current knowledge remains fragmented and often context-specific. Prior reviews have tended to focus on narrow applications, limiting their generalizability. For example, Yadav and Saini (2025) examined online shopping and focused on satisfaction, engagement and purchasing; Barari (2024) focused on gamified mobile apps and consumer experience; Santos et al. (2024) used bibliometric analysis to map research trends without addressing outcomes in depth and Tobon et al. (2020) focused on consumer decision-making in digital settings. While valuable, these reviews do not provide an integrative synthesis of gamification's theoretical foundations, empirical findings and marketing performance effects. Given the field's expansion and the resulting fragmentation of research, there is a clear need for a comprehensive synthesis that clarifies how gamification enhances marketing performance and identifies major trends and empirical gaps.
To address these gaps, this study conducts a systematic literature review (SLR) to answer two central research questions:
How has gamification in marketing been studied in terms of theories, contexts, characteristics and methodologies?
What are the effects of gamification on marketing performance?
2. Background
2.1 Gamification
Gamification refers to the integration of game elements into non-game contexts to motivate individuals and influence behavior (Deterding et al., 2011; Koivisto and Hamari, 2019). While games were originally designed for entertainment, research shows that game designs such as avatars and badges can also motivate skill development (Subrahmanyam and Greenfield, 1994) and facilitate behavior change (De Freitas, 2006). Building on these insights, industries outside of gaming, particularly marketing, have adopted game-like features into campaigns, loyalty programs and digital interfaces, giving rise to the concept of “gamification” (Deterding et al., 2011). Nowadays, gamification represents an umbrella concept encompassing diverse technologies and design practices aimed at providing gameful experiences that lead to desirable psychological and behavioral outcomes (Hamari, 2019).
Gamification designs can be classified in several ways. Werbach and Hunter's (2012) hierarchical framework distinguishes components, mechanics and dynamics. Chou's (2019) Octalysis Framework organizes gamification designs into eight motivational “core drives”, such as meaning, empowerment and social influence. Another widely adopted approach categorizes game elements into achievement-related, social-related and immersion-related elements (Koivisto and Hamari, 2019; Yang et al., 2023). This classification is adopted in the present study, as it aligns most closely with marketing objectives that seek to motivate and influence consumer behavior.
Additionally, this study considers advergames as a specialized form of gamification in marketing. Advergames are games created explicitly for marketing purposes, such as promoting products and conveying brand messages (Bellman et al., 2014). They, therefore, represent a particular implementation of gamification, as the entire game experience is intentionally designed to achieve marketing objectives. This design differs from embedding product or brand information into an existing entertainment-oriented game – commonly referred to as in-game advertising (Terlutter and Capella, 2013). Given that this review focuses on marketing activities that intentionally design gameful experiences to influence consumer responses, in-game advertising falls outside the scope of the study.
For clarity, gamification in this paper is defined as “the application of game elements, video games designed for marketing purposes, or other designs that create gameful experiences in marketing activities. These designs support consumers in generating value, optimizing experiences, and encouraging engagement, ultimately impacting marketing performance related to products, services, and brands”.
2.2 Marketing performance
Gamification has the potential to enhance marketing performance by increasing consumer engagement, fulfilling psychological needs (e.g. competence and relatedness) and enhancing perceived value (e.g. hedonic and utilitarian) (Yu and Huang, 2022).
Marketing performance refers to the extent to which marketing activities contribute to business success, with an emphasis on the economic value they generate (Hanssens and Pauwels, 2016). To evaluate and improve such performance, companies employ a range of context-specific metrics. Among these, cost-effectiveness and profitability (often conceptualized as marketing productivity) are particularly critical (O'Sullivan and Abela, 2007). Key dimensions of marketing productivity include consumer acquisition, retention, purchase and loyalty (Rust et al., 2004). Brand equity complements and facilitates these behavioral indicators, especially in situations where consumers have limited access to product information (Datta et al., 2017). Collectively, these behavioral and perceptual outcomes represent the financial and customer-relationship performance through which marketing ultimately contributes to firm profits (Moorman and Rust, 1999).
Since gamification is employed as a marketing tool to influence consumer behavior and, ultimately, increase profitability, these marketing performance dimensions provide informative indicators for evaluating its effectiveness. Accordingly, this study focuses on dimensions related to marketing productivity and brand equity. This focus does not imply that consumer experience and value are neglected; rather, they are treated as important psychological mechanisms and are reviewed as mediating factors within the reviewed studies.
3. Methodology
This study is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021). The literature search was conducted on January 1, 2025, via the Scopus database, known for its comprehensive coverage across disciplines, including science, technology and social science (Elsevier, 2025). The search string used was: ((TITLE-ABS-KEY (advergame) OR TITLE-ABS-KEY (gamif*)) AND (TITLE-ABS-KEY (market* OR consum* OR customer* OR purchas* OR buy* OR commerce OR sale OR sell* OR business OR shop* OR service* OR product* OR promotion* OR advert* OR brand*))) AND (LIMIT-TO (DOCTYPE, "cp") OR LIMIT-TO (DOCTYPE, "ar") OR LIMIT-TO (DOCTYPE, "ch")) AND (LIMIT-TO (LANGUAGE, "English")). The term "gamif*" was chosen to encompass variations derived from the root, including "gamification" and “gamify”. The rest of the keywords were used to include literature related to potential marketing outcomes. The search terms were applied to title-abstract-keywords, focusing on peer-reviewed papers in journals, conferences and book chapters.
Figure 1 outlines the steps of the review process [1]. For the reviewed paper, see Web Appendix 1.
The flow diagram is arranged from top to bottom with rectangular boxes connected by arrows. On the far left, three vertically oriented labels read “Identification”, “Screening”, and “Included”. At the top center, a box reads “Step 1: Records identified from Scopus on January 1, 2025 (n equals 4970)”. A rightward arrow points to a box reading “Records removed before screening: Duplicate records removed (n equals 28)”. A downward arrow points to a second box. The second box reads “Step 2: Records screened (n equals 4942)”. A rightward arrow points to a box reading “Records excluded: a) No relation to either gamification or marketing (n equals 378); b) Only related to gamification or marketing (n equals 3610); c) No relation to marketing performance defined in this study (n equals 772)”. A downward arrow points to a third box. The third box reads “Step 3: Papers assessed for eligibility (n equals 182)”. A rightward arrow points to a box reading “Papers excluded: Literature review (n equals 19); Not full paper (research plans, n equals 3); No access (n equals 15)”. A downward arrow points to a fourth box. The fourth box reads “Step 4: Forward and backward search were conducted (n equals 145)”. A rightward arrow points to a box reading “Records included: Forward and backward search (n equals 4)”. A downward arrow points to a final box. The final box reads “Studies included in review (n equals 149)”. The first box is under “Identification”. The second, third, and fourth boxes are under “Screening”. The final box is under “Included”.Literature review process. Source(s): Authors' own work
The flow diagram is arranged from top to bottom with rectangular boxes connected by arrows. On the far left, three vertically oriented labels read “Identification”, “Screening”, and “Included”. At the top center, a box reads “Step 1: Records identified from Scopus on January 1, 2025 (n equals 4970)”. A rightward arrow points to a box reading “Records removed before screening: Duplicate records removed (n equals 28)”. A downward arrow points to a second box. The second box reads “Step 2: Records screened (n equals 4942)”. A rightward arrow points to a box reading “Records excluded: a) No relation to either gamification or marketing (n equals 378); b) Only related to gamification or marketing (n equals 3610); c) No relation to marketing performance defined in this study (n equals 772)”. A downward arrow points to a third box. The third box reads “Step 3: Papers assessed for eligibility (n equals 182)”. A rightward arrow points to a box reading “Papers excluded: Literature review (n equals 19); Not full paper (research plans, n equals 3); No access (n equals 15)”. A downward arrow points to a fourth box. The fourth box reads “Step 4: Forward and backward search were conducted (n equals 145)”. A rightward arrow points to a box reading “Records included: Forward and backward search (n equals 4)”. A downward arrow points to a final box. The final box reads “Studies included in review (n equals 149)”. The first box is under “Identification”. The second, third, and fourth boxes are under “Screening”. The final box is under “Included”.Literature review process. Source(s): Authors' own work
4. Findings
In total, 149 papers comprising 172 empirical studies were included in the analysis. Following the Theory-Context-Characteristics-Methods (TCCM) framework (Paul and Rosado-Serrano, 2019), the following subsections address RQ1 by examining the theories employed, business contexts explored, variables analyzed and research methods utilized. The subsequent section addresses RQ2 by analyzing how different gamification forms influence marketing performance.
4.1 Methods used in gamification research
In the extant literature, surveys dominate (Figure 2), followed by laboratory experiments. In contrast, other methodologies, including interviews, mixed methods, panel data analysis and longitudinal studies, remain underutilized. This imbalance indicates research gaps in understanding gamification's effects in real-world settings and how they evolve over time.
The legend on the right lists eight categories: “Survey”, “Laboratory experiment”, “Field experiment”, “Quasi experiment”, “Interview”, “Mixed method”, “Panel data analysis”, and “Longitudinal study”. The pie chart is divided into eight slices, each labeled with a numeric value. Starting at the top and moving clockwise, the largest slice corresponds to “Survey” with a value of 108. The next largest slice corresponds to “Laboratory experiment” with a value of 39. A smaller slice corresponds to “Field experiment” with a value of 11. Two equal-sized smaller slices correspond to “Quasi experiment” and “Interview”, each with a value of 4, and “Mixed method” has the value of 3. Another smaller slice corresponds to “Panel data analysis” with a value of 2. The smallest slice corresponds to “Longitudinal study” with a value of 1.Methods used in gamification research. Source(s): Authors' own work
The legend on the right lists eight categories: “Survey”, “Laboratory experiment”, “Field experiment”, “Quasi experiment”, “Interview”, “Mixed method”, “Panel data analysis”, and “Longitudinal study”. The pie chart is divided into eight slices, each labeled with a numeric value. Starting at the top and moving clockwise, the largest slice corresponds to “Survey” with a value of 108. The next largest slice corresponds to “Laboratory experiment” with a value of 39. A smaller slice corresponds to “Field experiment” with a value of 11. Two equal-sized smaller slices correspond to “Quasi experiment” and “Interview”, each with a value of 4, and “Mixed method” has the value of 3. Another smaller slice corresponds to “Panel data analysis” with a value of 2. The smallest slice corresponds to “Longitudinal study” with a value of 1.Methods used in gamification research. Source(s): Authors' own work
The geographic distribution of data collection in the reviewed studies is notably uneven, and there is a strong concentration of research conducted in Asia, particularly in China, India and Indonesia (Figure 3). Several factors may explain this imbalance. First, leading e-commerce platforms in Asia (e.g. Shopee, Taobao and Temu) frequently adopt gamification designs during shopping festivals, providing resources for empirical investigation. Second, the demographic dividend of Asia's sizable populations can magnify the commercial impact of gamification, incentivizing both practitioners and scholars to engage in this area. However, this geographic concentration may introduce regional bias and limit the generalizability of findings, highlighting the need for more balanced evidence across cultural contexts in future research.
The diagram is arranged in two sections: a world map at the top and a bar chart at the bottom. At the top, a world map displays countries shaded in varying intensities of blue. China appears in the darkest shade. Other countries, including the United States, India, Indonesia, Taiwan, and several European and Asian countries, appear in lighter shades, while many regions remain unshaded. Below the map, a bar chart presents the number of articles by country or region. The horizontal axis is labeled “Country or region” and lists 27 countries and one additionally category at the end as “Not specific”. The vertical axis is labeled “Number of articles” and ranges from 0 to 40 in increments of 5 units. The data for the bars from left to right are as follows: For “Australia”, “Number of articles”: 1. For “Austria”, “Number of articles”: 1. For “Belgium”, “Number of articles”: 1. For “China”, “Number of articles”: 35. For “Egypt”, “Number of articles”: 1. For “Flemish”, “Number of articles”: 1. For “Germany”, “Number of articles”: 5. For “India”, “Number of articles”: 19. For “Indonesia”, “Number of articles”: 17. For “Iran”, “Number of articles”: 6. For “Ireland”, “Number of articles”: 1. For “Italy”, “Number of articles”: 3. For “Japan”, “Number of articles”: 1. For “Jordan”, “Number of articles”: 1. For “Macedonia”, “Number of articles”: 1. For “Malaysia”, “Number of articles”: 4. For “Morocco”, “Number of articles”: 1. For “Netherlands”, “Number of articles”: 2. For “Portugal”, “Number of articles”: 4. For “South Korea”, “Number of articles”: 2. For “Spain”, “Number of articles”: 2. For “Sweden”, “Number of articles”: 2. For “Taiwan”, “Number of articles”: 12. For “Turkey”, “Number of articles”: 2. For “United Kingdom”, “Number of articles”: 3. For “United States”, “Number of articles”: 14. For “Vietnam”, “Number of articles”: 1. For “Not specific”, “Number of articles”: 33.Countries and regions. Source(s): Authors' own work
The diagram is arranged in two sections: a world map at the top and a bar chart at the bottom. At the top, a world map displays countries shaded in varying intensities of blue. China appears in the darkest shade. Other countries, including the United States, India, Indonesia, Taiwan, and several European and Asian countries, appear in lighter shades, while many regions remain unshaded. Below the map, a bar chart presents the number of articles by country or region. The horizontal axis is labeled “Country or region” and lists 27 countries and one additionally category at the end as “Not specific”. The vertical axis is labeled “Number of articles” and ranges from 0 to 40 in increments of 5 units. The data for the bars from left to right are as follows: For “Australia”, “Number of articles”: 1. For “Austria”, “Number of articles”: 1. For “Belgium”, “Number of articles”: 1. For “China”, “Number of articles”: 35. For “Egypt”, “Number of articles”: 1. For “Flemish”, “Number of articles”: 1. For “Germany”, “Number of articles”: 5. For “India”, “Number of articles”: 19. For “Indonesia”, “Number of articles”: 17. For “Iran”, “Number of articles”: 6. For “Ireland”, “Number of articles”: 1. For “Italy”, “Number of articles”: 3. For “Japan”, “Number of articles”: 1. For “Jordan”, “Number of articles”: 1. For “Macedonia”, “Number of articles”: 1. For “Malaysia”, “Number of articles”: 4. For “Morocco”, “Number of articles”: 1. For “Netherlands”, “Number of articles”: 2. For “Portugal”, “Number of articles”: 4. For “South Korea”, “Number of articles”: 2. For “Spain”, “Number of articles”: 2. For “Sweden”, “Number of articles”: 2. For “Taiwan”, “Number of articles”: 12. For “Turkey”, “Number of articles”: 2. For “United Kingdom”, “Number of articles”: 3. For “United States”, “Number of articles”: 14. For “Vietnam”, “Number of articles”: 1. For “Not specific”, “Number of articles”: 33.Countries and regions. Source(s): Authors' own work
4.2 Theories and models
Theories and models were grouped into five overarching categories: personal behavior-related, technology adoption-related, mass communication-related, social-related and game design-related (Table 1). Example studies illustrating each theory and model are provided in Web Appendix 2. Notably, many theories have been applied only once, indicating a need for deeper investigation in this field. In the following subsections, we elaborate on the theories most commonly employed in prior studies. These theoretical perspectives also serve as the foundation for interpreting the empirical patterns discussed in later sections.
Theories applied in the selected studies
| Theories and models . | Count . |
|---|---|
| Personal behavior-related theories (n = 92) | |
| Cognitive-related (n = 48) | |
| Stimulus-organism-response model | 16 |
| Elaboration likelihood model | 9 |
| Construal level theory | 4 |
| Regulatory focus theory | 4 |
| Theory of planned behavior | 4 |
| Persuasion knowledge model | 2 |
| Affect-as-information theory | 1 |
| Belief-attitude-behavior model | 1 |
| Cognitive evaluation theory | 1 |
| Expectation–confirmation model | 1 |
| Hierarchy of effect theory | 1 |
| Limited capacity model of attention | 1 |
| Limited capacity model of mediated processing | 1 |
| Prospect theory | 1 |
| Self-construal theory | 1 |
| Motivational-related theories (n = 44) | |
| Self-determination theory | 27 |
| Flow theory | 11 |
| Perceived value theory | 2 |
| Associative-propositional evaluation model | 1 |
| Excitation transfer theory | 1 |
| Organismic integration theory | 1 |
| Protection motivation theory | 1 |
| Technology adoption-related theories (n = 17) | |
| Technology acceptance model | 14 |
| United theory of acceptance and use of technology | 2 |
| Modified technology acceptance model | 1 |
| Mass communication-related theories (n = 12) | |
| Use and gratifications theory | 9 |
| Media richness theory | 3 |
| Social-related theories (n = 5) | |
| Social cognition theory | 2 |
| Social exchange theory | 2 |
| Social presence theory | 1 |
| Game design-related theories (n = 4) | |
| Mechanics-dynamics-aesthetics model | 3 |
| Belief-attitude-behavior model | 1 |
| Theories and models . | Count . |
|---|---|
| Personal behavior-related theories (n = 92) | |
| Cognitive-related (n = 48) | |
| Stimulus-organism-response model | 16 |
| Elaboration likelihood model | 9 |
| Construal level theory | 4 |
| Regulatory focus theory | 4 |
| Theory of planned behavior | 4 |
| Persuasion knowledge model | 2 |
| Affect-as-information theory | 1 |
| Belief-attitude-behavior model | 1 |
| Cognitive evaluation theory | 1 |
| Expectation–confirmation model | 1 |
| Hierarchy of effect theory | 1 |
| Limited capacity model of attention | 1 |
| Limited capacity model of mediated processing | 1 |
| Prospect theory | 1 |
| Self-construal theory | 1 |
| Motivational-related theories (n = 44) | |
| Self-determination theory | 27 |
| Flow theory | 11 |
| Perceived value theory | 2 |
| Associative-propositional evaluation model | 1 |
| Excitation transfer theory | 1 |
| Organismic integration theory | 1 |
| Protection motivation theory | 1 |
| Technology adoption-related theories (n = 17) | |
| Technology acceptance model | 14 |
| United theory of acceptance and use of technology | 2 |
| Modified technology acceptance model | 1 |
| Mass communication-related theories (n = 12) | |
| Use and gratifications theory | 9 |
| Media richness theory | 3 |
| Social-related theories (n = 5) | |
| Social cognition theory | 2 |
| Social exchange theory | 2 |
| Social presence theory | 1 |
| Game design-related theories (n = 4) | |
| Mechanics-dynamics-aesthetics model | 3 |
| Belief-attitude-behavior model | 1 |
Personal behavior-related theories explain individual-level consumer behaviors, which are categorized as cognitive-related and motivational-related. Cognitive-related theories focus on how consumers process information and make decisions, which are defined as “emergent brain activities that exert determinative influence” (Bandura, 2001). The most widely used theory is the Stimulus-Organism-Response (SOR) model (Mehrabian and Russell, 1974), which conceptualizes gamification designs as stimuli, internal cognitive and affective states (e.g. engagement) as the organism and behavioral outcomes (e.g. purchase intentions) as the response. The model is widely used to examine how game elements influence consumers' psychology and behavior (e.g. Yu et al., 2022).
Motivational-related theories emphasize the psychological drivers of behavior. The most prominent is the Self-determination Theory (SDT), which posits that consumer engagement is shaped by the fulfillment of three basic psychological needs: autonomy (feeling in control), competence (feeling capable) and relatedness (feeling socially connected). Satisfaction of these needs influences both intrinsic and extrinsic motivation (Ryan and Deci, 2002). SDT has been widely used in studies of both game elements and video games, showing that gamification can satisfy these needs and thus positively affect behavioral intentions (e.g. Alvi, 2022).
Technology adoption-related theories examine how and why individuals adopt new technologies. The most frequently applied theory is the Technology Acceptance Model (TAM), which highlights perceived usefulness (PU) and perceived ease of use (PEOU) as primary drivers of technology adoption (Davis et al., 1989). PU concerns the belief that a technology will enhance performance, while PEOU addresses perceived simplicity of use. TAM is mainly used in studies of game elements, helping to explain consumer acceptance of gamified marketing systems and mobile applications (e.g. Aghdaie et al., 2022).
Mass communication-related theories focus on gamification as a media engagement tool. The Uses and Gratifications Theory (UGT) is the most commonly applied, emphasizing that individuals actively seek media experiences to satisfy specific psychological and social needs rather than being passive recipients (Katz et al., 1973). In gamification research, UGT explains consumer engagement for purposes such as entertainment, social interaction, information-seeking and personal gratification. UGT is particularly relevant to research on video games as marketing tools, offering insights into why consumers choose to participate in gamified experiences (e.g. Sreejesh et al., 2021).
Social-related theories encompass theoretical perspectives that explore consumer interaction within social environments. Game design-related theories focus on the fundamental principles and mechanisms underlying gamification designs. Compared to other groups, these two groups have received less attention.
4.3 Contexts
This section reviews the business contexts in which gamification has been applied (Tables 2 and 3). Across the dataset, studies span seven broad contexts, with some addressing multiple product categories (e.g. Müller-Stewens et al., 2017).
Product/service contexts and game elements
| Context . | Product/service . | Game elements . | ||||
|---|---|---|---|---|---|---|
| Achievement-related (n = 76) . | Social-related (n = 39) . | Immersion-related (n = 21) . | Miscellaneous (n = 36) . | Not specified (n = 32) . | ||
| Banking and financial services | ||||||
| Bank service | 6 | 2 | 1 | 1 | ||
| Green financial product | 1 | |||||
| Mobile payment service | 3 | 3 | 2 | 4 | 1 | |
| Food services (e.g. restaurants, cafes) | ||||||
| Coffee | 3 | |||||
| Food and drink | 1 | |||||
| Media and entertainment | ||||||
| AR-based game filter | 1 | 1 | ||||
| Esports live streaming | 1 | 1 | ||||
| Football game | 2 | 2 | ||||
| Online community (electronic device) | 2 | 2 | 2 | |||
| Premium service (Football fans club) | 4 | 1 | 1 | |||
| Social live streaming service | 1 | 1 | ||||
| User-generated content platform | 1 | |||||
| Retail | ||||||
| Apparel | 1 | 1 | 1 | 1 | ||
| Book | 2 | 1 | 1 | 2 | ||
| Car | 1 | 1 | ||||
| Chain store | 1 | |||||
| E-commerce live streaming | 2 | 2 | 2 | |||
| E-commerce platform (variety of products) | 15 | 8 | 3 | 5 | 11 | |
| Electronic device/component | 1 | 1 | 1 | |||
| Fashion product | 1 | 1 | ||||
| Furniture | 1 | 1 | 1 | |||
| Green consumption (green product/service) | 1 | 1 | 2 | |||
| Grocery food | 2 | 1 | 2 | |||
| Sports product | 1 | |||||
| Virtual product (metaverse) | 1 | |||||
| Transportation | ||||||
| Bicycle navigation service | 1 | |||||
| Sharing bike service | 1 | |||||
| Transportation services (e.g. taxis, public transportations) | 3 | 1 | ||||
| Travel and tourism | ||||||
| Green consumption (tourism) | 1 | 2 | 1 | |||
| Hotel | 1 | |||||
| Tourist service | 3 | 1 | 1 | 2 | 2 | |
| Well-being and health | ||||||
| Mental health service | 1 | |||||
| Professional health service | 1 | |||||
| Virtual coaching and training plans | 6 | 6 | 3 | 3 | 4 | |
| Miscellaneous | ||||||
| AI agent | 3 | 3 | ||||
| Education | 1 | 1 | 1 | |||
| Government service | 1 | |||||
| On-demand service | 1 | |||||
| Not specified | 6 | 2 | 3 | 2 | 5 | |
| Context . | Product/service . | Game elements . | ||||
|---|---|---|---|---|---|---|
| Achievement-related (n = 76) . | Social-related (n = 39) . | Immersion-related (n = 21) . | Miscellaneous (n = 36) . | Not specified (n = 32) . | ||
| Banking and financial services | ||||||
| Bank service | 6 | 2 | 1 | 1 | ||
| Green financial product | 1 | |||||
| Mobile payment service | 3 | 3 | 2 | 4 | 1 | |
| Food services (e.g. restaurants, cafes) | ||||||
| Coffee | 3 | |||||
| Food and drink | 1 | |||||
| Media and entertainment | ||||||
| AR-based game filter | 1 | 1 | ||||
| Esports live streaming | 1 | 1 | ||||
| Football game | 2 | 2 | ||||
| Online community (electronic device) | 2 | 2 | 2 | |||
| Premium service (Football fans club) | 4 | 1 | 1 | |||
| Social live streaming service | 1 | 1 | ||||
| User-generated content platform | 1 | |||||
| Retail | ||||||
| Apparel | 1 | 1 | 1 | 1 | ||
| Book | 2 | 1 | 1 | 2 | ||
| Car | 1 | 1 | ||||
| Chain store | 1 | |||||
| E-commerce live streaming | 2 | 2 | 2 | |||
| E-commerce platform (variety of products) | 15 | 8 | 3 | 5 | 11 | |
| Electronic device/component | 1 | 1 | 1 | |||
| Fashion product | 1 | 1 | ||||
| Furniture | 1 | 1 | 1 | |||
| Green consumption (green product/service) | 1 | 1 | 2 | |||
| Grocery food | 2 | 1 | 2 | |||
| Sports product | 1 | |||||
| Virtual product (metaverse) | 1 | |||||
| Transportation | ||||||
| Bicycle navigation service | 1 | |||||
| Sharing bike service | 1 | |||||
| Transportation services (e.g. taxis, public transportations) | 3 | 1 | ||||
| Travel and tourism | ||||||
| Green consumption (tourism) | 1 | 2 | 1 | |||
| Hotel | 1 | |||||
| Tourist service | 3 | 1 | 1 | 2 | 2 | |
| Well-being and health | ||||||
| Mental health service | 1 | |||||
| Professional health service | 1 | |||||
| Virtual coaching and training plans | 6 | 6 | 3 | 3 | 4 | |
| Miscellaneous | ||||||
| AI agent | 3 | 3 | ||||
| Education | 1 | 1 | 1 | |||
| Government service | 1 | |||||
| On-demand service | 1 | |||||
| Not specified | 6 | 2 | 3 | 2 | 5 | |
Product/service contexts and video game genres
| Context . | Product/service . | Video game genres . | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Racing game (n = 10) . | Action game (n = 9) . | Simulation game (n = 7) . | Sports game (n = 5) . | Strategy game (n = 3) . | Adventure game (n = 2) . | Puzzle game (n = 2) . | AR adventure game (n = 1) . | Role-playing game (n = 1) . | Shooting game (n = 1) . | Not specified (n = 6) . | ||
| Banking and financial services | ||||||||||||
| Credit card | 1 | |||||||||||
| Food services (e.g. restaurants, cafes) | ||||||||||||
| Bread | 1 | |||||||||||
| Food and drink | 1 | |||||||||||
| Retail | ||||||||||||
| Apparel | 1 | |||||||||||
| Bag | 1 | |||||||||||
| Bike | 2 | |||||||||||
| Car | 4 | |||||||||||
| Cereal | 3 | 1 | ||||||||||
| Cookie | 2 | |||||||||||
| E-commerce platform (variety products) | 1 | 1 | ||||||||||
| Energy drink | 1 | 1 | ||||||||||
| Healthy food | 1 | |||||||||||
| Mineral water | 1 | |||||||||||
| Pet food | 1 | |||||||||||
| Potato chip | 2 | |||||||||||
| Sports product | 5 | |||||||||||
| Toy | 1 | |||||||||||
| Transportation | ||||||||||||
| Campus map | 1 | |||||||||||
| Travel and tourism | ||||||||||||
| Tourist service | 1 | |||||||||||
| Not specified | 1 | 3 | 2 | 1 | 1 | 5 | ||||||
| Context . | Product/service . | Video game genres . | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Racing game (n = 10) . | Action game (n = 9) . | Simulation game (n = 7) . | Sports game (n = 5) . | Strategy game (n = 3) . | Adventure game (n = 2) . | Puzzle game (n = 2) . | AR adventure game (n = 1) . | Role-playing game (n = 1) . | Shooting game (n = 1) . | Not specified (n = 6) . | ||
| Banking and financial services | ||||||||||||
| Credit card | 1 | |||||||||||
| Food services (e.g. restaurants, cafes) | ||||||||||||
| Bread | 1 | |||||||||||
| Food and drink | 1 | |||||||||||
| Retail | ||||||||||||
| Apparel | 1 | |||||||||||
| Bag | 1 | |||||||||||
| Bike | 2 | |||||||||||
| Car | 4 | |||||||||||
| Cereal | 3 | 1 | ||||||||||
| Cookie | 2 | |||||||||||
| E-commerce platform (variety products) | 1 | 1 | ||||||||||
| Energy drink | 1 | 1 | ||||||||||
| Healthy food | 1 | |||||||||||
| Mineral water | 1 | |||||||||||
| Pet food | 1 | |||||||||||
| Potato chip | 2 | |||||||||||
| Sports product | 5 | |||||||||||
| Toy | 1 | |||||||||||
| Transportation | ||||||||||||
| Campus map | 1 | |||||||||||
| Travel and tourism | ||||||||||||
| Tourist service | 1 | |||||||||||
| Not specified | 1 | 3 | 2 | 1 | 1 | 5 | ||||||
Retail emerges as the most frequently explored context, reflecting the sector's rapid adoption of innovative consumer engagement strategies. E-commerce platforms like Taobao and Blibli have garnered significant academic attention. For instance, during large-scale shopping events, retailers often implement gamification designs (e.g. badges) to encourage consumer interaction with product pages, making browsing more engaging and thereby increasing customer retention and purchase intentions. In contrast, video games are used less frequently in e-commerce, likely due to the challenge of designing a single game that effectively promotes diverse brands or products. Generalized gaming experiences risk diluting brand-specific messaging and diverting attention from individual products, potentially reducing sales effectiveness. Video games are more common in specific product categories, such as automobiles and sports equipment, where interactive simulations align naturally with product characteristics.
The second most studied context is banking and financial services, including daily payment systems and banking-related transactions. Here, game elements are more prevalent than video games, particularly in mobile payment services, with a notable uptake in the Chinese market (e.g. Zhou et al., 2022). For example, Alipay rewards users with points for completing daily transactions, which can be redeemed for activities like tree planting that support real-world environmental initiatives, making the payment process more interactive while promoting sustained engagement.
The well-being and health sector is another prominent context, primarily in the area of virtual coaching and fitness applications such as Nike Run Club, where physical training often demands sustained motivation and effort. For example, achievement-related elements satisfy users' needs for accomplishment, while social features stimulate continued use and foster loyalty (e.g. Zhu et al., 2021).
Our findings also indicate that emerging trends further expand the scope of gamified marketing (Figure 4 and Table 2). First, gamification in the banking and financial services sector continues to grow alongside the global shift toward mobile transactions (Statista, 2025a). Second, live streaming has become an increasingly gamified context, including live-streamed gaming, social networking and real-time commerce. This growth is largely driven by the expansion of social media platforms that facilitate video streaming. Platforms like TikTok, YouTube and Instagram now offer live-streaming features that enable brands and influencers to interact with consumers in more immersive and engaging ways. Game elements such as virtual gifts and fan-level progression foster real-time interaction, enhancing participation and purchase intentions (e.g. Nguyen-Viet and Nguyen, 2024). Third, the green economy is emerging as a new frontier context for the use of gamification. Given the significant impact of consumer choices on environmental sustainability, companies are increasingly using game elements such as eco-rewards and carbon footprint tracking to motivate environmentally responsible purchasing behaviors (e.g. Chen et al., 2024).
The horizontal axis is labeled “Year” and includes the following categories from left to right: “2010”, “2013”, “2014”, “2015”, “2016”, “2017”, “2018”, “2019”, “2020”, “2021”, “2022”, “2023”, and “2024”. The vertical axis is labeled “Count” and ranges from 0 to 35 in increments of 5 units. A legend appears on the right with nine categories. Colored segments represent “Banking and financial services”, “Food services (e.g., restaurants, cafes)”, “Media and entertainment”, “Retail”, “Transportation”, “Travel and tourism”, “Well-being and health”, “Miscellaneous”, and “Not specific”. The data for the stacked bars are as follows: For the category “2010”, “Not specific”: 1. For the category “2013”, “Retail”: 3 and “Not specific”: 1. For the category “2014”, “Banking and financial services”: 1, “Retail”: 2, and “Not specific”: 2. For the category “2015”, “Transportation”: 1. For the category “2016”, “Banking and financial services”: 2, “Retail”: 3, and “Not specific”: 1. For the category “2017”, “Food services (e.g., restaurants, cafes)”: 1, “Retail”: 8, and “Not specific”: 3. For the category “2018”, “Retail”: 5, “Well-being and health”: 1, and “Not specific”: 1. For the category “2019”, “Media and entertainment”: 1, “Retail”: 6, “Well-being and health”: 1, and “Not specific”: 1. For the category “2020”, “Banking and financial services”: 1, “Food services (e.g., restaurants, cafes)”: 2, “Media and entertainment”: 1, “Retail”: 6, and “Well-being and health”: 1. For the category “2021”, “Banking and financial services”: 1, “Media and entertainment”: 2, “Retail”: 14, “Transportation”: 1, “Travel and tourism”: 2, “Well-being and health”: 6, and “Not specific”: 3. For the category “2022”, “Banking and financial services”: 4, “Food services (e.g., restaurants, cafes)”: 2, “Media and entertainment”: 3, “Retail”: 12, “Transportation”: 1, “Travel and tourism”: 2, “Well-being and health”: 2, “Miscellaneous”: 1, and “Not specific”: 5. For the category “2023”, “Banking and financial services”: 2, “Food services (e.g., restaurants, cafes)”: 1, “Media and entertainment”: 4, “Retail”: 6, “Transportation”: 1, “Travel and tourism”: 4, “Well-being and health”: 2, and “Not specific”: 2. For the category “2024”, “Banking and financial services”: 8, “Media and entertainment”: 1, “Retail”: 10, “Transportation”: 2, “Travel and tourism”: 1, “Well-being and health”: 1, “Miscellaneous”: 5, and “Not specific”: 3.Gamified product/service contexts distribution over the years. Source(s): Authors' own work
The horizontal axis is labeled “Year” and includes the following categories from left to right: “2010”, “2013”, “2014”, “2015”, “2016”, “2017”, “2018”, “2019”, “2020”, “2021”, “2022”, “2023”, and “2024”. The vertical axis is labeled “Count” and ranges from 0 to 35 in increments of 5 units. A legend appears on the right with nine categories. Colored segments represent “Banking and financial services”, “Food services (e.g., restaurants, cafes)”, “Media and entertainment”, “Retail”, “Transportation”, “Travel and tourism”, “Well-being and health”, “Miscellaneous”, and “Not specific”. The data for the stacked bars are as follows: For the category “2010”, “Not specific”: 1. For the category “2013”, “Retail”: 3 and “Not specific”: 1. For the category “2014”, “Banking and financial services”: 1, “Retail”: 2, and “Not specific”: 2. For the category “2015”, “Transportation”: 1. For the category “2016”, “Banking and financial services”: 2, “Retail”: 3, and “Not specific”: 1. For the category “2017”, “Food services (e.g., restaurants, cafes)”: 1, “Retail”: 8, and “Not specific”: 3. For the category “2018”, “Retail”: 5, “Well-being and health”: 1, and “Not specific”: 1. For the category “2019”, “Media and entertainment”: 1, “Retail”: 6, “Well-being and health”: 1, and “Not specific”: 1. For the category “2020”, “Banking and financial services”: 1, “Food services (e.g., restaurants, cafes)”: 2, “Media and entertainment”: 1, “Retail”: 6, and “Well-being and health”: 1. For the category “2021”, “Banking and financial services”: 1, “Media and entertainment”: 2, “Retail”: 14, “Transportation”: 1, “Travel and tourism”: 2, “Well-being and health”: 6, and “Not specific”: 3. For the category “2022”, “Banking and financial services”: 4, “Food services (e.g., restaurants, cafes)”: 2, “Media and entertainment”: 3, “Retail”: 12, “Transportation”: 1, “Travel and tourism”: 2, “Well-being and health”: 2, “Miscellaneous”: 1, and “Not specific”: 5. For the category “2023”, “Banking and financial services”: 2, “Food services (e.g., restaurants, cafes)”: 1, “Media and entertainment”: 4, “Retail”: 6, “Transportation”: 1, “Travel and tourism”: 4, “Well-being and health”: 2, and “Not specific”: 2. For the category “2024”, “Banking and financial services”: 8, “Media and entertainment”: 1, “Retail”: 10, “Transportation”: 2, “Travel and tourism”: 1, “Well-being and health”: 1, “Miscellaneous”: 5, and “Not specific”: 3.Gamified product/service contexts distribution over the years. Source(s): Authors' own work
4.4 Characteristics
4.4.1 Gamification forms
Game elements: Game elements were grouped into achievement-related, social-related and immersion-related categories. Elements that did not fit neatly into these groups were classified as miscellaneous (Table 4). Some elements may overlap across categories (e.g. leaderboards can foster both competition and achievement), illustrating the coding challenges that have affected both this review and prior literature.
Game elements used in the selected studies
| Achievement (n = 197) . | Count . | Social (n = 61) . | Count . | Immersion (n = 34) . | Count . | Miscellaneous (n = 72) . | Count . |
|---|---|---|---|---|---|---|---|
| Rewards (e.g. discounts, cash, bonuses), prizes (excluding badges), incentives | 41 | Competition, comparison | 16 | Avatars (2D, 3D), characters, virtual identities, profiles | 12 | Quizzes | 11 |
| Challenges, tasks, goals, purposes, missions | 29 | Approval of others, social interactions (e.g. feedback from friends), social presence | 15 | Customization, personalization | 9 | App-embedded mini-games | 9 |
| Points, scores, XP | 27 | Social networking features, social connections, social support mechanisms, communities | 12 | Narratives, storytelling | 9 | Gamified money (e.g. red packets) | 4 |
| Badges, achievements, medals, trophies | 25 | Cooperation, collaboration, teams | 8 | Immersion-related game elements (not specified) | 4 | Virtual trees | 4 |
| Leaderboards, rankings, standings, high score lists | 23 | Sharing features | 3 | Virtual goods | 2 | ||
| Progress, status bars, staging | 16 | Social-related game elements (not specified) | 7 | Virtual pets | 2 | ||
| Levels | 14 | Countdown timers | 1 | ||||
| Virtual currencies, coins | 8 | Face filters | 1 | ||||
| Performance feedback, graphs, tracking data (e.g. running distance) | 6 | Gamified chatbots | 1 | ||||
| Achievement-related game elements (not specified) | 8 | Scratch cards | 1 | ||||
| Spin wheels | 1 | ||||||
| Time-spent tracking | 1 | ||||||
| VIP memberships | 1 | ||||||
| Virtual worlds | 1 | ||||||
| Not specified | 32 |
| Achievement (n = 197) . | Count . | Social (n = 61) . | Count . | Immersion (n = 34) . | Count . | Miscellaneous (n = 72) . | Count . |
|---|---|---|---|---|---|---|---|
| Rewards (e.g. discounts, cash, bonuses), prizes (excluding badges), incentives | 41 | Competition, comparison | 16 | Avatars (2D, 3D), characters, virtual identities, profiles | 12 | Quizzes | 11 |
| Challenges, tasks, goals, purposes, missions | 29 | Approval of others, social interactions (e.g. feedback from friends), social presence | 15 | Customization, personalization | 9 | App-embedded mini-games | 9 |
| Points, scores, XP | 27 | Social networking features, social connections, social support mechanisms, communities | 12 | Narratives, storytelling | 9 | Gamified money (e.g. red packets) | 4 |
| Badges, achievements, medals, trophies | 25 | Cooperation, collaboration, teams | 8 | Immersion-related game elements (not specified) | 4 | Virtual trees | 4 |
| Leaderboards, rankings, standings, high score lists | 23 | Sharing features | 3 | Virtual goods | 2 | ||
| Progress, status bars, staging | 16 | Social-related game elements (not specified) | 7 | Virtual pets | 2 | ||
| Levels | 14 | Countdown timers | 1 | ||||
| Virtual currencies, coins | 8 | Face filters | 1 | ||||
| Performance feedback, graphs, tracking data (e.g. running distance) | 6 | Gamified chatbots | 1 | ||||
| Achievement-related game elements (not specified) | 8 | Scratch cards | 1 | ||||
| Spin wheels | 1 | ||||||
| Time-spent tracking | 1 | ||||||
| VIP memberships | 1 | ||||||
| Virtual worlds | 1 | ||||||
| Not specified | 32 |
Achievement-related elements are the most widely implemented. SDT provides a strong theoretical lens here, as it posits that individuals have a fundamental need for competence, which these elements satisfy by enabling consumers to experience progress, mastery and accomplishment. Within this category, “rewards/prizes/incentives” plays a central role in gamified marketing, extending previous insights (Koivisto and Hamari, 2019). This suggests that the suitability of game elements is context-dependent and reflects the increasing commercialization of gamification, where rewards like coupons are used strategically to attract consumers. However, such rewards may be less effective in contexts like education or workplace settings, where intrinsic motivation and skill development are more central (Huitt, 2001).
Within the social-related category, “competition/comparison” is particularly prevalent, motivating consumers by allowing them to compete with others. The Theory of Planned Behavior (TPB) highlights how subjective norms (perceptions of others' expectations) influence attitudes and behavioral intentions (Ajzen, 1991), providing a foundation for understanding elements such as competition and social approval. Interestingly, the element of “cooperation/collaboration/teams” is used less frequently in marketing than in other domains (Koivisto and Hamari, 2019), possibly because marketing strategies are often tailored to individual consumer incentives rather than collective goals.
Within the immersion-related category, the most extensively studied elements are “avatars/characters/virtual identities/profiles”. These elements foster emotional and psychological connections between consumers and brands, creating more personalized and memorable experiences. Flow Theory offers a compelling explanation: flow is a state of deep absorption, and elements like avatars can facilitate this state by increasing perceived control and emotional involvement (Csikszentmihalyi, 2013). This category receives less attention than others; however, the growing adoption of extended reality (XR) technologies is likely to accelerate its application in marketing. As XR becomes increasingly integrated into marketing strategies, immersive features like avatars, virtual assistants and interactive customization are expected to play increasingly important roles (Riar et al., 2023).
Video game genres: Of the reviewed studies, 43 investigated video games (examples are provided in Web Appendix 3), four of which investigated two games each. Games were classified following Statista (2022), with the distribution shown in Figure 5.
The horizontal axis is labeled “Video game genre” and lists the following categories from left to right: “Racing Game”, “Action Game”, “Simulation Game”, “Sports Game”, “Strategy Game”, “Adventure Game”, “Puzzle Game”, “AR adventure Game”, “Role-playing Game”, “Shooting game”, and “Unclear”. The vertical axis is labeled “Number of articles” and ranges from 0 to 12 in increments of 2 units. The data for the bars are as follows: For the category “Racing Game”, “Number of articles”: 10. For the category “Action Game”, “Number of articles”: 9. For the category “Simulation Game”, “Number of articles”: 7. For the category “Sports Game”, “Number of articles”: 5. For the category “Strategy Game”, “Number of articles”: 3. For the category “Adventure Game”, “Number of articles”: 2. For the category “Puzzle Game”, “Number of articles”: 2. For the category “AR adventure Game”, “Number of articles”: 1. For the category “Role-playing Game”, “Number of articles”: 1. For the category “Shooting game”, “Number of articles”: 1. For the category “Unclear”, “Number of articles”: 6.Video game genres used in the reviewed studies. Source(s): Authors' own work
The horizontal axis is labeled “Video game genre” and lists the following categories from left to right: “Racing Game”, “Action Game”, “Simulation Game”, “Sports Game”, “Strategy Game”, “Adventure Game”, “Puzzle Game”, “AR adventure Game”, “Role-playing Game”, “Shooting game”, and “Unclear”. The vertical axis is labeled “Number of articles” and ranges from 0 to 12 in increments of 2 units. The data for the bars are as follows: For the category “Racing Game”, “Number of articles”: 10. For the category “Action Game”, “Number of articles”: 9. For the category “Simulation Game”, “Number of articles”: 7. For the category “Sports Game”, “Number of articles”: 5. For the category “Strategy Game”, “Number of articles”: 3. For the category “Adventure Game”, “Number of articles”: 2. For the category “Puzzle Game”, “Number of articles”: 2. For the category “AR adventure Game”, “Number of articles”: 1. For the category “Role-playing Game”, “Number of articles”: 1. For the category “Shooting game”, “Number of articles”: 1. For the category “Unclear”, “Number of articles”: 6.Video game genres used in the reviewed studies. Source(s): Authors' own work
Racing games are the most commonly studied genre, particularly in the automotive context, where their competitive mechanics align naturally with product categories such as cars. By embedding brands into immersive racing settings, these games strengthen consumer–brand relationships and reinforce brand identity (Berger et al., 2018). Racing games may overlap with simulation games, given their shared emphasis on replicating real-world driving experiences. Action games are the second most frequently examined. Featuring fast-paced challenges (e.g. fighting), they deliver high-energy experiences that promote presence and flow. For instance, players need to defeat enemies to collect in-game resources, a mechanic that can be strategically used to reinforce brand incentives and research links such experiences to improved brand attitude and purchase intentions (Soebandhi et al., 2018). Simulation games replicate real-world activities, making them highly relevant for marketing. They allow consumers to interact with products, services and brands in authentic and relatable ways while also offering novel experiences unattainable in reality. For example, the plumber city game lets players engage in tasks without real-world consequences, creating memorable and psychologically impactful experiences. Such experiences can enhance brand recall and long-term memory associations (Sreejesh et al., 2021).
A critical insight concerns the alignment between game genres and product categories. Research indicates that games with high product–content congruence are more effective in enhancing brand memory and recognition than less congruent pairings (Gross, 2010).
4.4.2 Mediators
Table 5 summarizes the mediators that explain how gamification shapes consumer behavioral intentions. These mediators fall into three categories: consumer value-related, system-related and product-, service- and brand-related.
Summarization of the mediators
| Upper category . | Count . | Upper category . | Count . | ||
|---|---|---|---|---|---|
| System-related (n = 90) | Consumer value-related (n = 130) | ||||
| Gameful experience (n = 47) | Engagement with the gamified system | 10 | Hedonic (n = 31) | Perceived enjoyment | 18 |
| Flow | 9 | Hedonic value (general) | 13 | ||
| Playfulness | 7 | Utilitarian (n = 27) | Utilitarian value (general) | 24 | |
| Product/service experience (general) | 6 | Service quality | 3 | ||
| Presence | 5 | Social (n = 21) | Social connection | 6 | |
| Immersion | 4 | Social influence | 5 | ||
| Perceived interactivity | 4 | Social interaction | 3 | ||
| Involvement level | 2 | Normative community pressure | 2 | ||
| Perception of the design (n = 31) | Perceived ease of use | 10 | Conformity with social network | 1 | |
| Perceived vividness | 3 | Recognition | 1 | ||
| Attractiveness of the gamified system | 2 | Relatedness | 1 | ||
| Emotional attachment with the gamified service | 2 | Psychological contract | 1 | ||
| Perceived competence | 2 | Subjective norm | 1 | ||
| Perceived effectiveness | 2 | Motivation (n = 35) | Intrinsic need | 13 | |
| Performance of the system | 2 | Psychological need (general) | 6 | ||
| System design | 2 | Extrinsic need | 4 | ||
| Advergame content | 1 | Intention to engage with gamified system | 4 | ||
| Perceived challenge | 1 | Customer commitment | 2 | ||
| Perceived control | 1 | Cognitive fit | 1 | ||
| Social innovativeness | 1 | Habit | 1 | ||
| Hedonic innovativeness | 1 | Self-identification | 1 | ||
| Website characteristics | 1 | Self-presentation | 1 | ||
| Attitude (n = 7) | Attitude towards advergame | 7 | Sensation seeking | 1 | |
| Information characteristics (n = 5) | Persuasion knowledge | 3 | Willingness to use | 1 | |
| Product/service information | 2 | Other (n = 16) | Consumer perceived value (general) | 10 | |
| Product-, service- and brand-related (n = 68) | Consumer curiosity | 2 | |||
| Product/Service (n = 45) | Satisfaction | 19 | Environmental concern | 1 | |
| Engagement with products/services | 13 | Financial value | 1 | ||
| Attitude toward products/services | 7 | Knowledge acquisition | 1 | ||
| Trust | 3 | Reciprocal benefit | 1 | ||
| Perceived advantage of the product/service | 2 | Miscellaneous (n = 4) | |||
| Identification with product/service | 1 | Expectancy | 1 | ||
| Brand (n = 23) | Brand engagement | 17 | Transportability | 1 | |
| Brand love | 3 | Review usefulness | 1 | ||
| Brand placement acceptance | 1 | Word of mouth | 1 | ||
| Brand recall | 1 | ||||
| Self-brand congruity | 1 | ||||
| Upper category . | Count . | Upper category . | Count . | ||
|---|---|---|---|---|---|
| System-related (n = 90) | Consumer value-related (n = 130) | ||||
| Gameful experience (n = 47) | Engagement with the gamified system | 10 | Hedonic (n = 31) | Perceived enjoyment | 18 |
| Flow | 9 | Hedonic value (general) | 13 | ||
| Playfulness | 7 | Utilitarian (n = 27) | Utilitarian value (general) | 24 | |
| Product/service experience (general) | 6 | Service quality | 3 | ||
| Presence | 5 | Social (n = 21) | Social connection | 6 | |
| Immersion | 4 | Social influence | 5 | ||
| Perceived interactivity | 4 | Social interaction | 3 | ||
| Involvement level | 2 | Normative community pressure | 2 | ||
| Perception of the design (n = 31) | Perceived ease of use | 10 | Conformity with social network | 1 | |
| Perceived vividness | 3 | Recognition | 1 | ||
| Attractiveness of the gamified system | 2 | Relatedness | 1 | ||
| Emotional attachment with the gamified service | 2 | Psychological contract | 1 | ||
| Perceived competence | 2 | Subjective norm | 1 | ||
| Perceived effectiveness | 2 | Motivation (n = 35) | Intrinsic need | 13 | |
| Performance of the system | 2 | Psychological need (general) | 6 | ||
| System design | 2 | Extrinsic need | 4 | ||
| Advergame content | 1 | Intention to engage with gamified system | 4 | ||
| Perceived challenge | 1 | Customer commitment | 2 | ||
| Perceived control | 1 | Cognitive fit | 1 | ||
| Social innovativeness | 1 | Habit | 1 | ||
| Hedonic innovativeness | 1 | Self-identification | 1 | ||
| Website characteristics | 1 | Self-presentation | 1 | ||
| Attitude (n = 7) | Attitude towards advergame | 7 | Sensation seeking | 1 | |
| Information characteristics (n = 5) | Persuasion knowledge | 3 | Willingness to use | 1 | |
| Product/service information | 2 | Other (n = 16) | Consumer perceived value (general) | 10 | |
| Product-, service- and brand-related (n = 68) | Consumer curiosity | 2 | |||
| Product/Service (n = 45) | Satisfaction | 19 | Environmental concern | 1 | |
| Engagement with products/services | 13 | Financial value | 1 | ||
| Attitude toward products/services | 7 | Knowledge acquisition | 1 | ||
| Trust | 3 | Reciprocal benefit | 1 | ||
| Perceived advantage of the product/service | 2 | Miscellaneous (n = 4) | |||
| Identification with product/service | 1 | Expectancy | 1 | ||
| Brand (n = 23) | Brand engagement | 17 | Transportability | 1 | |
| Brand love | 3 | Review usefulness | 1 | ||
| Brand placement acceptance | 1 | Word of mouth | 1 | ||
| Brand recall | 1 | ||||
| Self-brand congruity | 1 | ||||
Consumer value-related mediators are the most extensively studied, focusing on the value consumers derive from gamified experiences. These mediators are typically divided into hedonic value and utilitarian value, reflecting the dual nature of gamification in offering both fun and functionality. Hedonic value relates to intrinsic motivations such as fun, excitement and enjoyment. Game elements like challenges, rewards and social interaction enhance perceived enjoyment, which in turn increases consumers' intentions to continue using products or services (Tsou et al., 2024). Utilitarian value refers to functional benefits, strengthened by both intangible incentives (e.g. progression levels) and tangible rewards (e.g. discounts) (Bravo et al., 2023). This duality reveals a key managerial insight: effective gamification must deliver both enjoyment and functional value to generate sustained engagement. The SOR framework provides a theoretical explanation for these value-related mediators, which represent the “organism” component through which gamification stimuli influence consumer responses (Shao et al., 2019).
System-related mediators focus on the experiences brought by the design, interactivity and informational content of gamified systems. Common constructs include system engagement and PEOU. These mediators capture the quality of interaction with the gamification designs. The TAM model explains several of these mediators by positing that PEOU shapes individuals' acceptance of gamified systems, which subsequently influences their behavioral intentions (Qian et al., 2023). This theoretical link highlights the importance of interface design, usability and system clarity in shaping gamification's effectiveness.
Product-, service- and brand-related mediators directly link gamification to consumers' perceptions of marketplace offerings. Constructs such as satisfaction and brand engagement illustrate how gamification enriches traditionally functional consumer interactions by making them more interactive and enjoyable. This transformation fosters deeper consumer involvement, which has been associated with higher brand affinity and more frequent usage behaviors (Ebrahimi et al., 2024). Flow Theory provides a central theoretical foundation for these mediators. When consumers enter a state of optimal experience, they become more absorbed in the gamified interaction. This heightened absorption triggers affective and cognitive responses that strengthen engagement with products, services and brands, which in turn shape behavioral intentions (Berger et al., 2018).
4.4.3 Moderators
A diverse set of moderators has been identified and categorized into five groups (Table 6).
Summarization of the moderators
| Moderator . | Count . | Moderator . | Count . | Moderator . | Count . |
|---|---|---|---|---|---|
| Intervention-related (n = 18) | Demographic-related (n = 10) | Marketing-related (n = 2) | |||
| Prevention focus | 3 | Age | 7 | Reward type | 2 |
| Promotion focus | 3 | Gender | 3 | Miscellaneous (n = 3) | |
| Involvement | 2 | Personal-related (n = 10) | Time frame | 1 | |
| Potential outcome concern | 2 | Prior experience | 5 | Green advertising | 1 |
| Cognitive elaboration | 1 | Personal trait | 4 | Negative online reviews | 1 |
| Compulsory play | 1 | Social competition motivation | 1 | ||
| Independent self-construal | 1 | System-related (n = 8) | |||
| Perceived risk | 1 | Game difficulty | 3 | ||
| Protection motivation | 1 | Gaming device | 2 | ||
| Psychological distance | 1 | Illustrated gameplay | 1 | ||
| Psychological fear | 1 | Narrative affordance | 1 | ||
| Relevance | 1 | Time pressure | 1 | ||
| Moderator . | Count . | Moderator . | Count . | Moderator . | Count . |
|---|---|---|---|---|---|
| Intervention-related (n = 18) | Demographic-related (n = 10) | Marketing-related (n = 2) | |||
| Prevention focus | 3 | Age | 7 | Reward type | 2 |
| Promotion focus | 3 | Gender | 3 | Miscellaneous (n = 3) | |
| Involvement | 2 | Personal-related (n = 10) | Time frame | 1 | |
| Potential outcome concern | 2 | Prior experience | 5 | Green advertising | 1 |
| Cognitive elaboration | 1 | Personal trait | 4 | Negative online reviews | 1 |
| Compulsory play | 1 | Social competition motivation | 1 | ||
| Independent self-construal | 1 | System-related (n = 8) | |||
| Perceived risk | 1 | Game difficulty | 3 | ||
| Protection motivation | 1 | Gaming device | 2 | ||
| Psychological distance | 1 | Illustrated gameplay | 1 | ||
| Psychological fear | 1 | Narrative affordance | 1 | ||
| Relevance | 1 | Time pressure | 1 | ||
Demographic factors, particularly age and gender, are the most examined and their effects are highly context-specific. For instance, age has been shown to significantly moderate the impact of gamification on purchase intentions and loyalty, suggesting that older consumers respond differently to gamified marketing strategies compared to younger consumers (Butt et al., 2025). However, in other contexts such as travel intentions (Thirumaran et al., 2021) or brand attitude (Bellman et al., 2014), age exerts no significant moderating effect. Similarly, gender moderates the relationship between gamification and brand engagement (Sangroya et al., 2021), implying that men and women may engage with gamified experiences differently due to underlying psychological, social or motivational differences. However, in tourism contexts, no significant moderating effect of gender has been found (Thirumaran et al., 2021). These findings highlight the importance of customizing gamification strategies to specific consumer segments for optimal effectiveness.
Prior experience, encompassing knowledge and familiarity with specific activities, systems, products or services, is another critical moderator. Evidence shows that previous gaming experience and shopping experience significantly shape the relationship between gamification and key outcomes such as purchase intentions (De Canio et al., 2021). These findings suggest that gamification tends to be more effective when consumers possess prior familiarity with the underlying activity or context.
4.4.4 Main categories of marketing performance
This review synthesizes several key performance dimensions across products, services and brands (Figure 6). Product-related performance is captured through purchase-related aspects such as purchase intentions. Service-related performance emphasizes use-related aspects such as intentions to use. Brand-related performance is evaluated through several constructs: attitude reflects consumers' overall evaluation of brand-related information (Keller, 2003); awareness captures the extent to which consumers can recognize the brand (Keller, 1993) and attachment means the emotional bond between consumers and brands (Park et al., 2010). Brand equity, a higher-order construct encompassing consumers' perceptions, thoughts and feelings about a brand (Mizik and Jacobson, 2008), is listed separately because some studies conceptualize it as an umbrella construct that integrates multiple brand-related dimensions.
The diagram is arranged as a three-circle Venn diagram labeled “Brand”, “Product”, and “Service” with overlapping areas. At the top circle, labeled “Brand”, the following entries are listed: “Brand attitude (29)”, “Brand memory (6)”, “Self-brand connection (6)”, “Brand equity (5)”, “Brand awareness (4)”, and “Brand attachment (2)”. In the left circle, labeled “Product”, the entries are: “Purchase intention (45)”, “Impulsive consumption (6)”, “Repurchase intention (5)”, “Actual purchase (4)”, “Travel intention (2)”, “Adoption intention (1)”, “Continue shopping intention (1)”, “Intention to visit the restaurant (1)”, and “Unplanned purchase (1)”. In the right circle, labeled “Service”, the entries are: “Intention to use (23)”, “Continue use intention (21)”, and “Actual usage (5)”. In the central overlapping area, labeled “Product-, service-, and brand-related loyalty”, the entries are: “Loyalty (23)”, “Brand loyalty (11)”, “Brand trust, commitment, and satisfaction (2)”, “User retention (1)”, and “User stickiness (1)”.Dimensions of marketing performance. Source(s): Authors' own work
The diagram is arranged as a three-circle Venn diagram labeled “Brand”, “Product”, and “Service” with overlapping areas. At the top circle, labeled “Brand”, the following entries are listed: “Brand attitude (29)”, “Brand memory (6)”, “Self-brand connection (6)”, “Brand equity (5)”, “Brand awareness (4)”, and “Brand attachment (2)”. In the left circle, labeled “Product”, the entries are: “Purchase intention (45)”, “Impulsive consumption (6)”, “Repurchase intention (5)”, “Actual purchase (4)”, “Travel intention (2)”, “Adoption intention (1)”, “Continue shopping intention (1)”, “Intention to visit the restaurant (1)”, and “Unplanned purchase (1)”. In the right circle, labeled “Service”, the entries are: “Intention to use (23)”, “Continue use intention (21)”, and “Actual usage (5)”. In the central overlapping area, labeled “Product-, service-, and brand-related loyalty”, the entries are: “Loyalty (23)”, “Brand loyalty (11)”, “Brand trust, commitment, and satisfaction (2)”, “User retention (1)”, and “User stickiness (1)”.Dimensions of marketing performance. Source(s): Authors' own work
Loyalty occupies a central position across products, services and brands in Figure 6. Defined as a consumer's commitment to repurchase or repatronize a product, service or brand (Oliver, 1999). Loyalty is commonly measured through diverse indicators, including repurchase intentions, satisfaction and word-of-mouth (Xi and Hamari, 2020). To maintain conceptual clarity, this review treats loyalty as a broader construct, distinct from repurchase and continued use intentions.
Overall, this synthesis provides researchers with a structured map of underexplored areas and offers managers a practical checklist for evaluating the effectiveness of their gamified marketing strategies.
4.5 Empirical results of the effect of gamification
This section evaluates the effectiveness of different gamification designs on marketing performance. To ensure robustness, only studies with clear, reportable results were included and interview-based studies were excluded (Tables 7 and 8).
Among the selected studies, over half reported positive effects, while two found negative results (Matthew et al., 2021; Rauh et al., 2024). The remaining studies showed mixed results, categorized as “tend to be positive”, “null or equal positive and negative” or “tend to be negative”, depending on whether most findings were positive, insignificant or negative. Importantly, some game elements such as virtual worlds remain underexplored, limiting the generalizability of conclusions. Moreover, many studies assess multiple game elements simultaneously or treat gamification as a whole, making it difficult to isolate the effects of individual elements.
4.5.1 Main result 1: game elements generally exert a positive influence on marketing performance
Achievement-related elements are the most frequently implemented and typically promote positive behavioral intentions, though their effectiveness varies across designs and consumer segments. From an implementation standpoint, challenges that are too simple or too difficult can diminish engagement by inducing boredom or discouragement (Berger et al., 2018). This pattern aligns with Flow Theory, which posits that flow occurs when consumers' skills are well matched to the level of challenge and that deviations from this balance reduce the likelihood of flow and weaken gamification's behavioral influence (Csikszentmihalyi, 2013). Moreover, points and levels that lack tangible value risk being perceived as merely symbolic (Wildan et al., 2023), while their use as both rewards and penalties (e.g. adding points for retained products but deducting them for returns) may even produce negative effects (Rauh et al., 2024). Individual differences also contribute to variability in how consumers value different types of rewards (Matthew et al., 2021). SDT further explains this pattern: individuals have three basic psychological needs (autonomy, competence and relatedness), game elements that primarily target competence and autonomy (e.g. achievement elements) may be less effective for consumers who place greater emphasis on relatedness.
Social-related elements generally show positive effects as well. These elements can foster community spirit, strengthen social bonds and promote collaboration and shared playfulness, thereby increasing engagement and satisfying consumers' need for relatedness – ultimately enhancing marketing performance (Xi and Hamari, 2020). However, exceptions exist. Poorly designed competition (e.g. an unbalanced competitive structure) may backfire, leading to disappointment and disengagement (Aghdaie et al., 2022). SDT helps explain this pattern: when competition threatens individuals' sense of competence, intrinsic motivation declines, reducing the overall effectiveness of gamification designs. Contextual factors also influence outcomes. For instance, in mobile health applications, social interaction may not generate positive effects because some users are reluctant to share sensitive health information, raising concerns about privacy and surveillance (Biasiotto et al., 2023).
Immersion-related elements allow consumers to create virtual personas and engage in self-presentation, which supports escapism, offers a temporary break from reality and fosters stronger emotional connections – all of which positively influence marketing performance (Aghdaie et al., 2022). Construal Level Theory further explains these effects by suggesting that avatars and narratives reduce psychological distance, making the brand appear more personally relevant and thereby enhancing behavioral intentions. However, there are exceptions. For example, one study found that profile features reduced purchase intention, although this effect may have been driven by contextual factors such as stricter return policies rather than the feature itself (Rauh et al., 2024).
4.5.2 Main result 2: video games designed for marketing purposes (i.e. advergames) are generally effective marketing tools
Advergames provide a complete gaming experience and, as a result, can engage consumers deeply while fostering enjoyment without imposing financial pressure. Despite these benefits, several considerations are important. First, voluntary engagement is crucial. Forced or unintentional exposure can reduce their impact (Berger et al., 2018). This pattern aligns with SDT, which emphasizes autonomy as a fundamental psychological need; when consumers feel deprived of choice, intrinsic motivation declines. Second, effectiveness depends on perceived transparency. When consumers realize that a game functions primarily as a promotional tool, persuasion knowledge may be activated and gameplay expectations may be violated, reducing satisfaction and brand favorability (Soebandhi et al., 2018). Third, advergames may backfire for unfamiliar brands, as negative or aversive game content can transfer undesirable emotional associations to the brand (Waiguny et al., 2013). Importantly, little evidence exists on advergames' impact on long-term outcomes such as loyalty, highlighting a methodological gap.
4.6 Ethical concerns of gamification in marketing
While gamification can enhance marketing performance, it also raises ethical concerns that warrant careful consideration (Ebrahimi et al., 2024; Zhou et al., 2022). Three consumer-related issues are particularly salient: autonomy, privacy and fairness.
4.6.1 Autonomy
Reward systems and competitive structures leverage psychological mechanisms that influence consumer behavior, sometimes beyond consumers' conscious awareness (Al-Msallam et al., 2023). This can blur the line between motivation and manipulation, particularly when gamification prioritizes sales maximization over consumer welfare. A key risk is impulsive consumption, defined as an unplanned purchase made without prior purchase intention (Beatty and Ferrell, 1998). Evidence from live-streaming and online shopping contexts shows that elements like rewards and badges are positively associated with impulse and unplanned purchases (Zhang et al., 2021). These effects emerge partly because gamified experiences heighten positive emotional states (Floh and Madlberger, 2013). Moreover, gamification may amplify impulse purchase intentions by fostering “swift guanxi” (rapidly formed and emotionally charged interpersonal bonds) (Nguyen-Viet and Nguyen, 2024). Together, these mechanisms highlight the autonomy-related ethical dilemma posed by gamified marketing.
4.6.2 Privacy
Gamification systems collect, track and analyze large amounts of behavioral data, raising concerns about potential misuse or unauthorized sharing of sensitive information (Al-Msallam et al., 2023). These concerns are particularly salient in contexts such as healthcare applications that track physical activity and medical-related behaviors. When consumers perceive their privacy as compromised, trust in digital marketing erodes (Goldfarb and Tucker, 2011). A growing awareness of intrusive data collection has already increased skepticism and distrust toward digital advertising (Obermiller and Spangenberg, 2000). This trend serves as a cautionary signal for gamified marketing strategies that rely heavily on user data.
4.6.3 Fairness
Fairness in gamification refers to ensuring that all consumers have an equal opportunity to participate, succeed and benefit from the experience, regardless of their background (Freeman et al., 2022). Standardized gamification designs may inadvertently disadvantage users with different skills and resources. For example, designs that are too easy for some but too difficult for others create uneven competition and risk disengaging participants through boredom or discouragement (Berger et al., 2018). When challenges are poorly calibrated, gamification can reinforce existing inequalities among users, creating ethical concerns about inclusivity and equal access to benefits.
5. Future research agenda
Drawing on the current literature, we propose 14 agenda points for future research across theoretical, thematic and methodological categories.
5.1 Theoretical agenda
5.1.1 Agenda point 1
Future research can explore the scope of gamified marketing and clarify what “gamified marketing” entails. While gamification is widely adopted in marketing campaigns, the concept is subject to varying interpretations. Some scholars restrict it to simple game elements, whereas others adopt a broader perspective that includes any “gameful” experience in non-gaming contexts. These divergent interpretations reflect an ongoing conceptual debate, highlighting a research gap regarding the boundaries and appropriate scope of gamified marketing. Although this review proposes a definition of “gamified marketing”, further progress in the field depends on the development of a more unified and operationalized definition. Future studies should trace the historical evolution of gamification in marketing to establish a widely accepted conceptualization, foster shared understanding among scholars and enhance comparability across studies.
5.1.2 Agenda point 2
Future research should incorporate a cultural–societal perspective. Current studies are heavily concentrated in Asian markets, with less attention paid to other cultural contexts. As globalization deepens, understanding how consumers' cultural identities shape their responses to gamified marketing becomes increasingly important, given that identity influences economic behaviors (Sreen et al., 2018). For instance, prior research reports divergent findings between Indonesia and Germany regarding the relationship between gamification’s hedonic value and consumer loyalty (Kristian and Napitupulu, 2022; Wolf, 2019). To advance the field, future studies should employ cultural theories to examine how gamification strategies operate across diverse cultural contexts.
5.1.3 Agenda point 3
Future research should adopt a balanced perspective by examining gamification's ethical risks alongside its commercial benefits. Gamified marketing raises concerns related to data privacy, consumer autonomy and fairness, creating significant ethical dilemmas. Yet, current literature offers limited insights into these risks and provides little guidance on how to design gamification responsibly. Future studies should therefore investigate the ethical challenges inherent in gamified marketing and develop managerial frameworks that balance commercial effectiveness with consumer welfare.
5.1.4 Agenda point 4
Future research should explore how gamification can be strategically aligned with the Sustainable Development Goals (SDGs). In today's business landscape, marketing extends beyond profit maximization to include sustainability objectives such as encouraging responsible consumption, which is essential for the long-term well-being of present and future generations. Although some studies have explored gamification's role in promoting sustainable consumption, most focus on conventional business contexts. Important dilemmas remain, particularly regarding whether gamification can reduce barriers associated with sustainable products, including higher prices, while effectively shaping consumer decision-making. Future research should examine how gamification can be strategically designed to achieve the SDGs.
5.1.5 Agenda point 5
Future research should delve deeper into the multifaceted nature of motivation induced by gamification, moving beyond the conventional intrinsic–extrinsic split. Most studies interpret gamification's motivational effects through SDT as either intrinsic or extrinsic. While this binary view provides valuable insights, it may overlook the processes of internalization and externalization. For instance, externally prompted behavior (e.g. social recognition) may become internalized and aligned with a consumer's self-concept over time, blurring the intrinsic-extrinsic distinction. Second, real-world motives often coexist, with a single interaction being both enjoyable (intrinsic) and status-enhancing (extrinsic). Future studies should consider the full SDT regulation continuum (external, introjected, identified, integrated), and draw on sub-theories such as Cognitive Evaluation Theory to explain how extrinsic and intrinsic motivation interact over time (Ryan and Deci, 2002).
5.1.6 Agenda point 6
Future research should probe the time-varying, cyclical nature of consumer interactions with gamified marketing. While the S-O-R framework has provided valuable foundations in the current literature, it may oversimplify real-world marketing experiences where responses become new stimuli. For example, leaderboards or rewards can convert yesterday's action (R) into today's trigger (S), shaping habits and expectations over time. Repeat purchasing and continued use typically arise from such iterative cycles rather than single exposures. Understanding these complex processes is crucial for more accurately reflecting real-world consumer behavior. Thus, future research should adopt alternative theoretical perspectives that model feedback and sequence effects, such as reinforcement learning and behavior control theory.
5.2 Thematic agenda
5.2.1 Agenda point 7
From a marketing-objective perspective, future research should examine how gamification can strengthen consumer–brand connections. Gamification has the potential to foster a sense of belonging and emotional attachment between consumers and brands when brands align with consumer identities, and such connections are instrumental in shaping favorable attitudes toward the brand (Hollebeek et al., 2014). However, evidence shows that not all game elements produce the same effect on self–brand connection (Alvi, 2022). Given that this area remains underexplored, future studies should investigate how gamification can be strategically designed to facilitate value co-creation and reinforce consumer–brand connections.
5.2.2 Agenda point 8
From a marketing-target perspective, future research should investigate how individual differences moderate the effectiveness of gamified marketing. Current studies often treat consumers as a homogeneous group, overlooking heterogeneity in behavioral responses. Extant research has indicated that rewards may strengthen consumer–brand relationships more among highly innovative consumers than among less innovative ones (Shankar, 2022). Understanding such heterogeneity would enable firms to craft more precise, segment-tailored strategies rather than one-size-fits-all designs. Future studies should model the moderating role of both general personal factors (e.g. personality traits) and game-related factors (e.g. gaming experience).
5.2.3 Agenda point 9
From a marketing-content perspective, future research should investigate personalized gamification enabled by AI-driven and robotic applications (e.g. AI chatbots). AI can detect behavioral patterns and predict individual preferences, enabling adaptive gamification that matches users' skills and preferences. This is crucial, as poorly calibrated designs can frustrate consumers and reduce effectiveness. AI can also enrich gamification designs. For example, non-player characters traditionally used in gaming have been adapted into AI-driven virtual assistants that tailor interactions to individual needs (Elmashhara et al., 2024). Future studies should therefore investigate how AI-powered gamification strategies impact consumer experience and marketing performance.
5.2.4 Agenda point 10
From a marketing-channel perspective, future research should investigate gamification in the metaverse and XR. XR technologies are expanding rapidly, with the market projected to reach USD 41.8 billion by 2028 (Statista, 2025b), and they hold significant potential for enhancing gamified marketing strategies. XR enables the creation of immersive holographic environments that transcend physical constraints and deliver more engaging consumer experiences. However, the integration of XR and gamification in marketing remains nascent, and its potential is still underexplored (Riar et al., 2023). Future research should assess how XR-based designs (e.g. avatars) influence customer experience and marketing performance.
5.3 Methodological agenda
5.3.1 Agenda point 11
Future research should incorporate more longitudinal studies to explore the effects of gamification on marketing performance over time. Most existing studies rely on surveys and experiments that capture short-term responses. These methods overlook the dynamic nature of consumer behavior and the possibility that gamification's impact may evolve, showing patterns of growth, decline or stability as consumer responses fluctuate (Koivisto and Hamari, 2019). Longitudinal methods such as repeated surveys and panel studies would enable researchers to track the same variables over extended periods. For example, monitoring spending on gamified shopping platforms over time could provide insights into whether sustained engagement with gamification leads to increased purchasing behavior.
5.3.2 Agenda point 12
Future research should employ more field studies, such as field experiments and digital behavior analysis, to evaluate the real-world effectiveness of gamification in marketing. Most current experiments are conducted in laboratory settings, often using hypothetical scenarios. While these ensure strong variable control, they may not fully capture the complexity and contextual nuances of real market environments (Harrison and List, 2004), even with careful efforts to balance internal and external validity. In contrast, field experiments capture authentic consumer behavior, often without participants' awareness that they are part of a study, thereby providing richer insights. Future research should complement laboratory findings with such field-based approaches to yield more comprehensive evidence on the effectiveness of gamified marketing.
5.3.3 Agenda point 13
Future research should integrate neuroscience methods, such as EEG (electroencephalography) and eye-tracking, to uncover the neural mechanisms underlying consumer experiences in gamified marketing. Existing studies predominantly rely on self-reported data, which are useful for capturing consumers' perceptions but are also constrained by memory limitations, subjective bias and an inability to capture moment-to-moment physiological responses. To address these limitations, future research should incorporate neuroscience methods to provide objective, real-time data on consumer reactions.
5.3.4 Agenda point 14
To enhance scalability and improve predictive capabilities, future research should adopt advanced data analytics approaches, including big data analysis, machine learning and natural language processing. Many companies have embedded gamification into their platforms. These initiatives generate vast amounts of behavioral data, yet such data remain underutilized in academic research. With advances in big data analytics, researchers can process large and complex datasets to uncover nuanced patterns of consumer interaction. Future studies can mine data from company platforms to identify how consumers engage with different gamification designs and which designs are most effective in driving desired marketing performance outcomes.
6. Conclusions and contributions
This review offers a comprehensive synthesis of 15 years of research on gamification in marketing, revealing both its significant potential and inherent complexities. While most studies show that gamification can enhance key marketing performance dimensions, notable exceptions, contextual contingencies and ethical concerns remain. These mixed patterns highlight the need for a more nuanced, evidence-based understanding of when, how and for whom gamification delivers meaningful marketing value. Figure 7 presents a conceptual framework derived from our analysis (Sections 4.4.1–4.4.4).
The conceptual framework is arranged from left to right with three main sections labeled “Gamification”, “Mediators”, and “Marketing performance”, with a horizontal section labeled “Moderators” at the bottom. On the left, a box labeled “Gamification” contains two sections. The first section is “Game elements” with the following entries: “Achievement-related (E.g., badge, points, leaderboards)”, “Social-related (E.g., competition, social networking, cooperation)”, “Immersion-related (E.g., avatar, customization, narrative)”, and “Miscellaneous (E.g., virtual tree, virtual pet, quiz)”. The second section is “Video games (E.g., racing, action, simulation)”. A rightward arrow points from this box to the Mediators section. The central section labeled “Mediators” contains multiple grouped categories. Under “System-related”, the entries are: “Gameful experience (E.g., flow, playfulness, presence)”, “Perception of the design (E.g., ease of use, vividness, game content)”, “Attitude (E.g., attitude towards advergame)”, and “Information characteristics (E.g., persuasion knowledge, product/service information)”. Below this, under “Product-, service-, and brand-related”, the entries are: “Product or service (E.g., satisfaction, trust, engagement with products)” and “Brand (E.g., brand love, brand recall, self-brand congruity)”. To the right, under “Consumer value-related”, the entries are: “Hedonic (E.g., enjoyment, hedonic value (general))”, “Utilitarian (E.g., service quality, utilitarian value (general))”, “Social (E.g., social connection, social influence, subjective norm)”, “Motivation (E.g., intrinsic need, extrinsic need, habit)”, “Economic (E.g., financial value)”, and “Other values (E.g., knowledge acquisition, reciprocal benefit, consumer curiosity)”. Below this, a section labeled “Miscellaneous” includes “E.g., review usefulness, word of mouth, expectancy”. A rightward arrow extends from the Mediators to the Marketing performance section. On the right, a box labeled “Marketing performance” contains the following categories: “Product (E.g., purchase intention, impulsive consumption, actual purchase)”, “Service (E.g., intention to use, continue use intention, actual usage)”, “Brand (E.g., brand attitude, equity, awareness)”, and “Loyalty (E.g., brand loyalty, user retention, user stickiness)”. At the bottom, a wide horizontal box labeled “Moderators” includes six grouped categories. Under “Intervention-related”, the entries are “prevention focus, involvement, compulsory play”. Under “Demographic-related”, the entries are “age, gender”. Under “Personal-related”, the entries are “prior experience, personal trait, social competition motivation”. Under “System-related”, the entries are “game difficulty, gaming device, time pressure”. Under “Marketing-related”, the entry is “reward type”. Under “Miscellaneous”, the entries are “time frame, green advertising, negative online reviews”. Two upward arrows extend from the Moderators and point to the path between “Gamification” and “Mediators” and the path between “Mediators” and “Marketing performance”, respectively.Conceptual framework of how gamification influences marketing performance. Source(s): Authors' own work
The conceptual framework is arranged from left to right with three main sections labeled “Gamification”, “Mediators”, and “Marketing performance”, with a horizontal section labeled “Moderators” at the bottom. On the left, a box labeled “Gamification” contains two sections. The first section is “Game elements” with the following entries: “Achievement-related (E.g., badge, points, leaderboards)”, “Social-related (E.g., competition, social networking, cooperation)”, “Immersion-related (E.g., avatar, customization, narrative)”, and “Miscellaneous (E.g., virtual tree, virtual pet, quiz)”. The second section is “Video games (E.g., racing, action, simulation)”. A rightward arrow points from this box to the Mediators section. The central section labeled “Mediators” contains multiple grouped categories. Under “System-related”, the entries are: “Gameful experience (E.g., flow, playfulness, presence)”, “Perception of the design (E.g., ease of use, vividness, game content)”, “Attitude (E.g., attitude towards advergame)”, and “Information characteristics (E.g., persuasion knowledge, product/service information)”. Below this, under “Product-, service-, and brand-related”, the entries are: “Product or service (E.g., satisfaction, trust, engagement with products)” and “Brand (E.g., brand love, brand recall, self-brand congruity)”. To the right, under “Consumer value-related”, the entries are: “Hedonic (E.g., enjoyment, hedonic value (general))”, “Utilitarian (E.g., service quality, utilitarian value (general))”, “Social (E.g., social connection, social influence, subjective norm)”, “Motivation (E.g., intrinsic need, extrinsic need, habit)”, “Economic (E.g., financial value)”, and “Other values (E.g., knowledge acquisition, reciprocal benefit, consumer curiosity)”. Below this, a section labeled “Miscellaneous” includes “E.g., review usefulness, word of mouth, expectancy”. A rightward arrow extends from the Mediators to the Marketing performance section. On the right, a box labeled “Marketing performance” contains the following categories: “Product (E.g., purchase intention, impulsive consumption, actual purchase)”, “Service (E.g., intention to use, continue use intention, actual usage)”, “Brand (E.g., brand attitude, equity, awareness)”, and “Loyalty (E.g., brand loyalty, user retention, user stickiness)”. At the bottom, a wide horizontal box labeled “Moderators” includes six grouped categories. Under “Intervention-related”, the entries are “prevention focus, involvement, compulsory play”. Under “Demographic-related”, the entries are “age, gender”. Under “Personal-related”, the entries are “prior experience, personal trait, social competition motivation”. Under “System-related”, the entries are “game difficulty, gaming device, time pressure”. Under “Marketing-related”, the entry is “reward type”. Under “Miscellaneous”, the entries are “time frame, green advertising, negative online reviews”. Two upward arrows extend from the Moderators and point to the path between “Gamification” and “Mediators” and the path between “Mediators” and “Marketing performance”, respectively.Conceptual framework of how gamification influences marketing performance. Source(s): Authors' own work
6.1 Theoretical contributions
First, this study provides the most comprehensive synthesis of gamification in marketing to date, reviewing 149 papers and consolidating fragmented knowledge while identifying critical gaps for future research. Second, it deepens the understanding of underlying mechanisms by demonstrating that gamification operates through three primary mediating pathways – consumer value-related, system-related and product-, service- and brand-related – while showing that moderators such as demographics and prior experience make its effects highly context-dependent. Third, it clarifies which game elements and advergame genres have received the most attention and evaluates their relative effectiveness in shaping marketing performance. Fourth, it foregrounds ethical issues by linking gamification to concerns about manipulation, privacy and fairness, underscoring the need to integrate ethics into gamified marketing practice. Fifth, it maps gamification across diverse product and service contexts, reinforcing its growing strategic importance.
6.2 Practical implications
The findings provide valuable implications for practitioners seeking to use gamification to enhance marketing effectiveness in a more ethical and sustainable way. Overall, the positive effects observed across many studies help build confidence in gamification's potential, even when acknowledging biases and failure cases in practice. Additionally, the proposed conceptual framework and future research agenda provide insights for marketers, platform designers and service providers in developing more effective gamified marketing strategies.
Specifically, the results indicate that gamification outcomes are influenced by multiple factors and that the motivational effects of different designs vary. Consequently, gamification should be carefully tailored to specific marketing objectives, taking into account product characteristics and consumer differences, rather than applied as a one-size-fits-all strategy. More importantly, the findings underscore the significance of consumer experiences and ethical responsibility. With the rapid advancement of virtual and AI-driven technologies, future gamified marketing practices are likely to feature greater immersiveness and personalization. These developments create new opportunities but also heighten challenges, emphasizing the need for responsible and consumer-centered designs. Ultimately, gamified marketing should balance motivational benefits with ethical considerations, ensuring that experiences remain both playful and meaningful.
6.3 Ethical considerations in gamification research
Ethically, this review underscores key concerns, including the potential erosion of consumer autonomy through impulsive purchasing, privacy and trust risks posed by extensive data collection and fairness issues arising from designs that may disadvantage certain users. Addressing these challenges requires regulatory compliance and the adoption of user-centered design strategies.
7. Limitations
First, while Scopus is the largest database of peer-reviewed literature, relying solely on this source may have led to the omission of relevant studies indexed elsewhere. Future studies should consider incorporating other databases such as JSTOR and WOS. Second, restricting the search to English-language papers may have overlooked insights from non-English studies. Future studies can expand the search to include additional languages to encompass a broader linguistic scope. Third, while this review aimed to capture a representative sample and used a broad search string, there may still be limitations related to keyword selection and search strategy. Therefore, future studies may seek to refine the search string. Fourth, while this SLR offers a broad overview and identifies research gaps, it does not quantitatively compare the effectiveness of specific gamification designs. Future studies can use meta-analytic methods to provide more granular insights into which elements consistently yield positive or negative effects, thereby enabling more evidence-based marketing strategies.
The early version of the work titled “Gamification and Marketing Management: A Literature Review and Future Agenda” was published in the proceedings of the Americas Conference on Information Systems (AMCIS) in 2023. This work has been supported by the Ministry of Education and Culture (Finland); the Business Finland Decision 6742/31/2023 (MetaMarketing); the Foundation for Economic Education under Grant No. 210301 (GAMETH); the Research Council of Finland Flagship Programme under Grant No. 337653 (UNITE). The authors used ChatGPT (OpenAI) for limited language editing during the preparation of this editorial and also received professional human proofreading. The authors reviewed all edits and take responsibility for the final content.
Note
PRISMA flow diagram https://www.prisma-statement.org/prisma-2020-flow-diagram
The supplementary material for this article can be found online.



